Keywords: AI/ML Image Reconstruction, Brain
Motivation: Noncontrast computed tomography(NCCT), commonly used for its rapidity in acute ischemic stroke(AIS) diagnosis, often fails to identify early ischemic changes, whereas MRI, despite its superior sensitivity , may introduce critical delays due to its lengthier image acquisition time.
Goal(s): This study aims to investigate the feasibility of converting NCCT images of AIS patients to diffusion-weighted (DW) images using deep learning techniques.
Approach: The proposed method utilizes an enhanced CycleGAN model to generate synthetic DW images from CT scans.
Results: The synthetic DW images generated by our network achieved good performance with a PSNR of 28.30, an SSIM of 0.843, and an NMSE of 0.293.
Impact: This work might be translated to clinical settings to help physicians make clinical decisions for patients by providing with high-quality MR images in emergency situations.
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